By using the wavelet transform, we develop a hierarchical planar curve descriptor that decomposes a curve into components of different scales so that the coarsest scale components carry the global approximation information while the finer scale components contain the local detailed information. We show that the wavelet descriptor has many desirable properties such as multiresolution representat...

This paper presents a novel multi scale gradient and a corner point based shape descriptors. The novel multi scale gradient based shape descriptor is combined with generic Fourier descriptors to extract contour and region based shape information. Shape information based object class detection and classification technique with a random forest classifier has been optimized. Proposed integrated de...

A very simple but efficient feature descriptor is proposed for image matching/registration applications where invariance is not important. The descriptor length is only three times the height of the local region in which the descriptor is calculated, and experiments were conducted to compare it to the SURF descriptor. In addition, it is shown, how the sampling can be modified in order to obtain...

The paper presents an efficient and reliable feature descriptor for human detection in a top-view depth image that uses two statistical values of mean and standard deviation. Human detection performance of our descriptor outperform Rauter that use mean value and Simplified Local Ternary Pattern (SLTP). To evaluate the human detection performance using our descriptor, we capture 559 positive and...

With the explosive growth of video capture capable mobile handsets and online visual data repositories, the popular query-by-capture applications call for a compact visual descriptor with minimum descriptor length. How to preserve the visual descriptor information while minimizing the bit rate for representing the descriptors, is a focus of the ongoing MPEG7 CDVS (Compact Descriptor for Visual ...

This paper presents a new methodology based on Genetic Programming that aims to create novel mathematical expressions that could improve local descriptors algorithms. We introduce the RDGPILLUM descriptor operator that was learned with two image pairs considering rotation, scale and illumination changes during the training stage. Such descriptor operator has a similar performance to our previou...

In this paper, we propose a novel shape descriptor that is robust in differentiating intrinsic symmetric points on geometric surfaces. Our motivation is that even the state-of-the-art shape descriptors and non-rigid surface matching algorithms suffer from symmetry flips. They cannot differentiate surface points that are symmetric or near symmetric. Hence a left hand of one human model may be ma...

In this paper we propose two novel rotation invariant local texture descriptors. They are based on Local Binary Pattern (LBP), which is one of the most effective and frequently used texture descriptor. Although LBP efficiently captures the local structure, it is not rotation invariant. In the proposed methods, a dominant direction is evaluated in a circular neighbourhood and the descriptor is c...